A case of fresh fruits and vegetables supply chain performance improvement: a system dynamics modelling and analysis
Bibliographic record
Abstract
Growth of agricultural food industry is critical to economy as it delivers the basic physiological necessity to human beings - food. Therefore, efficiency and effectiveness of fruits and vegetables supply chain (FVSC) has always attracted prominence. Motivation to this paper has been the live case of a food packaging industry - FoodFund located in London, Canada, which connects farmers to end-consumers through supply of fresh produce. Responsiveness, convenience, flexibility, affordability, product-freshness and sustainability in ecosystem are their six business pillars. In order to understand the key operational drivers and their inherent dynamics in FVSC, the author has applied value stream mapping (VSM) and system dynamics (SD) methodologies. The behavioural implications of the key drivers in FVSC were analysed using six SD based simulation scenarios. The dynamics amongst the four policy-drivers: fresh-produce procurement lead-time, packaging-velocity, finish-packed order dispatch-rate and transportation time to consumer, have provided useful insights for enhancing business growth and reducing variability in FVSC to the stakeholders.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".